library(tidyverse)
library(lubridate)
library(rvest)
Se importa la tabla “Compustat Global Daily” con los tipos de datos correctos.
global_daily <- read_csv("Compustat_Global_Daily.csv",
col_types = cols(
sedol = col_character(),
datadate = col_date(format = "%Y%m%d")
)
)
Tipo de variable de cada columna
global_daily %>%
summarise_all(class) %>%
pivot_longer(everything(), names_to = "column", values_to = "type")
Vista general
global_daily
global_daily %>%
count(curcdd)
observaciones de cada compañía
global_daily %>%
count(conm) %>%
arrange(conm)
global_daily %>%
count(conm) %>%
arrange(desc(n))
global_daily %>%
filter(curcdd == "EUR") %>%
count(conm)
global_daily %>%
filter(is.na(qunit)) %>%
count(conm)
global_daily %>%
count(gsector)
industries
global_daily %>%
group_by(conm) %>%
count(sic) %>%
arrange(sic)
global_daily %>%
count(sic)
global_daily %>%
count(conm) %>%
arrange(n) %>%
filter(n == 1)
global_daily %>%
select(conm, datadate) %>%
group_by(conm) %>%
mutate(n = n()) %>%
filter(n == 1)
global_daily %>%
select(datadate) %>%
distinct(datadate) %>%
arrange(datadate)
global_daily %>%
filter(conm == "WAL MART DE MEXICO SA") %>%
select(conm, datadate, cshoc, cshtrd, prccd, prcstd, gsector) %>%
mutate(year = year(datadate)) %>%
group_by(year) %>%
summarise(n = n())
global_daily %>%
filter(conm == "WAL MART DE MEXICO SA") %>%
select(conm, datadate, cshoc, cshtrd, prccd, prcstd, gsector) %>%
arrange(datadate)
global_daily %>%
filter(conm == "WAL MART DE MEXICO SA") %>%
mutate(year = year(datadate)) %>%
filter(year == 1993) %>%
arrange(datadate) %>%
filter(curcdd == "MXN")
global_daily %>%
count(exchg)
global_daily %>%
mutate(year = year(datadate)) %>%
#filter(conm == "CEMEX SAB DE CV") %>%
group_by(year, conm, isin) %>%
summarise(num = n())